Documentation for datasets containing the Outputs (results) of the Marginal Damages Model. There are two folders, which contain the same datasets but in two different formats (CSV and RData), so the files are also different. 

These datasets contain:
(i) Marginal damages (Marginal Values -- MV) per tonne of ground-level emissions of each species, in each county.
(ii) The source-receptor matrix (SRM) for marginal damages -- these are matrices MI described in Eq. 2 of the manuscript. They show the marginal damages occurring in each county as a consequence of a ground-level emission of 1 tonne in each county. The rows are the sources and the columns are the receptors, so that the row sums for these matrix are equivalent to the total marginal damages (i).

For both (i) and (ii), the damages are calculated for a combination of 80 different combinations of pollutant species, CRFs, Baseline ambient PM2.5 levels, and Mortality data. These are:

* 5 pollutant species: (Primary PM2.5, SO2, NOX, NH3, and VOC
* 4 CRFs: GEMM (Burnett et al., 2018), Vodonos et al. (2018) Parametric, Vodonos et al. (2018) Spline, and Krewski et al. (2009).
* 2 Baseline ambient PM2.5 levels: 2008 and 2017
* 2 Baseline Mortality Data: 

The marginal damages are 80 vectors of 3,108 rows, each representing one county.
The source-receptor matrices are 80 square (3,108 x 3,108) matrices, where the 3,108 rows represent the 3,108 source counties and the 3,108 columns represent the 3,108 receptor counties.

***ALL values are monetized damages (2017 USD) caused by emissions of 1 tonne (metric ton, or 10^6 grams) of a given species (depending on the dataset), in each source.

***The counties (rows in the MV files; both rows and columns in the SRM files) are presented in the same order of the Results_With_Emissions/Inputs/Auxiliary/STCOUList.csv file


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(1) CSV Files
There are 81 .csv files -- one for the marginal damages and 80 for the SRMs

(i) Marginal Values
The 80 MVs are saved as a single 3,108 x 81 matrix (the first column is just the row number)
Filename: MarginalValues_AllResults.csv
Rows are the sources (3,108 counties)
Columns are the combination of baseline PM Year, baseline mortality, CRF, and Pollutant, named:
MV_Damages_BasePM_[PMYear]_Mortality_[MYear]_[CRF]_[Pollutant]

Where:
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017
CRF is the CRF used (GEMM, Vodonos_Parametric, Vodonos_Spline, or Krewski)
Pollutant is one of Primary_PM2.5, SO2, NOX, NH3, or VOC

Element i,j represents the marginal damage per tonne of ground-level emissions of pollutant j (estimated with the respective CRF, baseline ambient PM2.5 concentration, and baseline mortality) in county i. 

(ii) Source-Receptor Matrices
Each of the 80 SRM and will be saved as an individual .csv file.
Each file is a 3,108 x 3,108 matrix, where rows are the sources (3,108 counties) and columns the receptors (3,108 counties)

Filenames are: SRM_Damages_BasePM_[PMYear]_Mortality_[MYear]_[CRF]_[Pollutant].csv

Where:
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017
CRF is the CRF used (GEMM, Vodonos_Parametric, Vodonos_Spline, or Krewski)
Pollutant is one of Primary_PM2.5, SO2, NOX, NH3, or VOC

Element i,j represents the marginal damage per tonne of emissions occurring in county j as a consequence of 1 tonne of ground-level emission in county i. For emissions: Pollutant j, and the CRF, baseline ambient PM2.5 concentration, and baseline mortality used are given by the file name.
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(2) RData Files
For .RData, there are 8 files -- 4 for the MVs and 4 for the SRMs.


(i) Marginal Values:
Filenames are: MV_Damages_BasePM_[PMYear]_Mortality_[MYear].RData

Where:
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017


Once loaded in R, each .RData file contains 4 "datasets"/"variables", one for each CRF. 
These "datasets" are in fact matrices of dimension 3,108 x 5 where each of the four objects is a matrix of 3,108 rows by 5 columns, where element i,j is the marginal damage per tonne of ground-level emissions of pollutant j in county i.
The rows are sources (3,108 counties) -- in the same order of the Results_With_Emissions/Inputs/Auxiliary/STCOUList.csv file

The 5 columns are one for each pollutant.
1: Primary PM2.5 
2: SO2
3: NOX
4: NH3
5: VOC

These matrices/variables are named:
Marg.Damages.[CRF]_BasePM_[PMYear]_Mortality_[MYear]

Where:
CRF is one of GEMM, Vodonos.Parametric, Vodonos.Spline, or Krewski
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017


(ii) Source-Receptor Matrix
Filenames are: SRM_Damages_BasePM_[PMYear]_Mortality_[MYear].RData

Where:
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017


Once loaded in R, each .RData file contains 4 "datasets"/"variables", one for each CRF. 
These "datasets" are in fact arrays of dimension 3,108 x 3,108 x 5 where:
The rows are sources (3,108 counties);
The columns are receptors (3,108 counties); and
The 5 slices are one for each pollutant. In order: Primary PM2.5, SO2, NOX, NH3, and VOC

These arrays/variables are named:
Marg.Damages.[CRF]_BasePM_[PMYear]_Mortality_[MYear]

Where:
CRF is one of GEMM, Vodonos.Parametric, Vodonos.Spline, or Krewski
PMYear is the year of baseline ambient PM 2.5 concentrations data, and can be 2008 or 2017
MYear is the year of baseline mortality data, and can be 2008 or 2017

Element i,j,k represents the marginal damage per tonne of emissions occurring in county j as a consequence of 1 tonne of ground-level emission of pollutant k in county i. The CRF, baseline ambient PM2.5 concentration, and baseline mortality used are given by the file and object names.
